| Abstract: | Canonical correlation has seen growing acceptance in geographical research as a tool for analysing the interrelationships between two sets of variables.1 It provides a natural extension to the multivariate case of simple correlation analysis introduced into the discipline in the fifties for measuring the degree of areal association between two individual variables.2 It has also proved valuable for forging a link between traditional geographic variables measuring the attributes of places and those indicating interactions among them.3 Recently, major developments in canonical theory have occurred which provide two major benefits for geographical research.4 First, asymmetrical regression relationships in addition to symmetrical correlation relationships between two variable sets can be determined. Researchers can use canonical regression to examine the degree to which one variable set is capable of predicting the other, in addition to canonical correlation which examines the symmetrical interrelationships between the two.5 Secondly, much improved methods are available for measuring the number, strength, and nature of the interrelationships between the two variable sets, and for assessing the adequacy of the canonical model in general.8 The purpose of this paper is to provide an overview of these developments and, more particularly, to explore their implications for the validity of empirical results obtained in earlier applications of canonical analysis. This is not intended as a criticism of these studies but rather as an attempt to further our understanding of spatial structure and process through re-examination of existing data in the light of refined techniques. |
